1.1 Setting up

Here are the commands I invoke to get ready to play with new data, including everything required to install hpgltools, the software it uses, and the fission data.

library(hpgltools)
tt <- sm(library(fission))
tt <- data(fission)

1.2 Data import

All the work I do in Dr. El-Sayed’s lab makes some pretty hard assumptions about how data is stored. As a result, to use the fission data set I will do a little bit of shenanigans to match it to the expected format. Now that I have played a little with fission, I think its format is quite nice and am likely to have my experiment class instead be a SummarizedExperiment.

## Extract the meta data from the fission dataset
meta <- as.data.frame(fission@colData)
## Make conditions and batches
meta$condition <- paste(meta$strain, meta$minute, sep=".")
meta$batch <- meta$replicate
meta$sample.id <- rownames(meta)
## Grab the count data
fission_data <- fission@assays$data$counts
## This will make an experiment superclass called 'expt' and it contains
## an ExpressionSet along with any arbitrary additional information one might want to include.
## Along the way it writes a Rdata file which is by default called 'expt.Rdata'
fission_expt <- create_expt(metadata=meta, count_dataframe=fission_data)

## Reading the sample metadata.

## The sample definitions comprises: 36 rows(samples) and 7 columns(metadata fields).

## Matched 7039 annotations and counts.

## Bringing together the count matrix and gene information.

2.1 Try using limma first

limma_comparison <- sm(limma_pairwise(fun_data))

names(limma_comparison$all_tables)

## [1] "wt30_vs_wt120"

summary(limma_comparison$all_tables$wt30_vs_wt120)

##      logFC           AveExpr            t             P.Value      
##  Min.   :-4.278   Min.   :-4.58   Min.   :-88.48   Min.   :0.0000  
##  1st Qu.:-0.399   1st Qu.: 1.11   1st Qu.: -2.60   1st Qu.:0.0192  
##  Median :-0.020   Median : 3.97   Median : -0.13   Median :0.1240  
##  Mean   : 0.008   Mean   : 3.11   Mean   : -0.17   Mean   :0.2792  
##  3rd Qu.: 0.300   3rd Qu.: 5.44   3rd Qu.:  1.72   3rd Qu.:0.4653  
##  Max.   : 7.075   Max.   :18.59   Max.   : 62.44   Max.   :1.0000  
##    adj.P.Val            B        
##  Min.   :0.0170   Min.   :-8.29  
##  1st Qu.:0.0767   1st Qu.:-6.58  
##  Median :0.2479   Median :-5.50  
##  Mean   :0.3686   Mean   :-4.87  
##  3rd Qu.:0.6204   3rd Qu.:-3.50  
##  Max.   :1.0000   Max.   : 4.83

scatter_wt_mut <- extract_coefficient_scatter(limma_comparison, type="limma",
                                              x="wt30", y="wt120")

## This can do comparisons among the following columns in the pairwise result:

## wt120, wt30

## Actually comparing wt30 and wt120.

scatter_wt_mut$scatter

scatter_wt_mut$both_histogram$plot + ggplot2::scale_y_continuous(limits=c(0, 0.20))

## Warning: Removed 7039 rows containing non-finite values (stat_bin).

## Warning: Removed 7039 rows containing non-finite values (stat_density).

## Warning: Removed 4 rows containing missing values (geom_bar).

## Warning: Removed 1 rows containing missing values (geom_vline).

ma_wt_mut <- extract_de_plots(limma_comparison, type="limma")
ma_wt_mut$ma$plot

ma_wt_mut$volcano$plot

2.2 Then DESeq2

deseq_comparison <- sm(deseq2_pairwise(fun_data))

summary(deseq_comparison$all_tables$wt30_vs_wt120)

##     baseMean           logFC            lfcSE            stat        
##  Min.   :      0   Min.   :-5.615   Min.   :0.000   Min.   :-20.800  
##  1st Qu.:     28   1st Qu.:-0.386   1st Qu.:0.168   1st Qu.: -1.176  
##  Median :    192   Median : 0.000   Median :0.222   Median :  0.000  
##  Mean   :   1703   Mean   : 0.020   Mean   :0.489   Mean   :  0.168  
##  3rd Qu.:    536   3rd Qu.: 0.343   3rd Qu.:0.412   3rd Qu.:  1.109  
##  Max.   :4924000   Max.   : 7.212   Max.   :4.072   Max.   : 30.370  
##     P.Value         adj.P.Val     
##  Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.0197   1st Qu.:0.0685  
##  Median :0.2503   Median :0.4676  
##  Mean   :0.3600   Mean   :0.4805  
##  3rd Qu.:0.6666   3rd Qu.:0.8732  
##  Max.   :1.0000   Max.   :1.0000

scatter_wt_mut <- extract_coefficient_scatter(deseq_comparison, type="deseq",
                                              x="wt30", y="wt120", gvis_filename=NULL)

## This can do comparisons among the following columns in the pairwise result:

## wt120, wt30, r2, r3

## Actually comparing wt30 and wt120.

scatter_wt_mut$scatter

plots_wt_mut <- extract_de_plots(deseq_comparison, type="deseq")
plots_wt_mut$ma$plot

plots_wt_mut$volcano$plot

2.3 EdgeR

edger_comparison <- sm(edger_pairwise(fun_data, model_batch=TRUE))

plots_wt_mut <- extract_de_plots(edger_comparison, type="edger")
scatter_wt_mut <- extract_coefficient_scatter(edger_comparison, type="edger",
                                              x="wt30", y="wt120", gvis_filename=NULL)

## This can do comparisons among the following columns in the pairwise result:

## wt120, wt30

## Actually comparing wt30 and wt120.

scatter_wt_mut$scatter

plots_wt_mut$ma$plot

plots_wt_mut$volcano$plot

2.4 EBSeq

ebseq_comparison <- sm(ebseq_pairwise(fun_data))
head(ebseq_comparison$all_tables[[1]])

##              ebseq_FC    logFC ebseq_postfc ebseq_mean     PPEE    PPDE
## SPAC1002.01    0.5391 -0.89135       0.5554     11.405 0.468257 0.53174
## SPAC1002.02    1.0571  0.08007       1.0567     88.884 0.917728 0.08227
## SPAC1002.03c   0.8580 -0.22094       0.8581   1637.860 0.694695 0.30531
## SPAC1002.04c   1.2573  0.33028       1.2566    223.063 0.568149 0.43185
## SPAC1002.05c   1.6914  0.75825       1.6888    188.029 0.002677 0.99732
## SPAC1002.06c   2.1381  1.09636       1.9456      4.165 0.587048 0.41295
##              ebseq_adjp
## SPAC1002.01    0.468257
## SPAC1002.02    0.917728
## SPAC1002.03c   0.694695
## SPAC1002.04c   0.568149
## SPAC1002.05c   0.002677
## SPAC1002.06c   0.587048

2.5 My stupid basic comparison

basic_comparison <- sm(basic_pairwise(fun_data))
summary(basic_comparison$all_tables$wt30_vs_wt120)

##  numerator_median denominator_median numerator_var      denominator_var   
##  Min.   :-2.73    Min.   :-3.60      Length:5505        Length:5505       
##  1st Qu.: 3.31    1st Qu.: 3.31      Class :character   Class :character  
##  Median : 4.65    Median : 4.63      Mode  :character   Mode  :character  
##  Mean   : 4.71    Mean   : 4.71                                           
##  3rd Qu.: 5.94    3rd Qu.: 5.93                                           
##  Max.   :18.61    Max.   :18.61                                           
##        t               p                 logFC            adjp          
##  Min.   :-50.21   Length:5505        Min.   :-4.263   Length:5505       
##  1st Qu.: -2.10   Class :character   1st Qu.:-0.406   Class :character  
##  Median : -0.39   Mode  :character   Median :-0.070   Mode  :character  
##  Mean   : -0.16                      Mean   : 0.008                     
##  3rd Qu.:  1.53                      3rd Qu.: 0.297                     
##  Max.   : 49.10                      Max.   : 7.485

scatter_wt_mut <- extract_coefficient_scatter(basic_comparison, type="basic",
                                              x="wt30", y="wt120", gvis_filename=NULL)

## This can do comparisons among the following columns in the pairwise result:

## wt120, wt30

## Actually comparing wt30 and wt120.

## Did not find wt30 or wt120.

scatter_wt_mut$scatter

## NULL

plots_wt_mut <- extract_de_plots(basic_comparison, type="basic")
plots_wt_mut$ma$plot

plots_wt_mut$volcano$plot

2.6 Combine them all

all_comparisons <- sm(all_pairwise(fun_data, model_batch=TRUE, parallel=FALSE))

all_combined <- sm(combine_de_tables(all_comparisons, excel=FALSE))
head(all_combined$data[[1]], n=3)

##              deseq_logfc deseq_adjp edger_logfc edger_adjp limma_logfc
## SPAC1002.01     -1.08000     0.3664    -1.05900     0.2201    -0.99860
## SPAC1002.02     -0.01485     0.9816    -0.02342     1.0000     0.03778
## SPAC1002.03c    -0.22760     0.2327    -0.23630     0.1598    -0.33910
##              limma_adjp basic_nummed basic_denmed basic_numvar
## SPAC1002.01     0.16930        0.000        0.000            0
## SPAC1002.02     0.99460        3.100        2.774    3.603e-01
## SPAC1002.03c    0.02432        6.909        7.248    2.955e-03
##              basic_denvar basic_logfc  basic_t   basic_p basic_adjp
## SPAC1002.01             0       0.000  0.00000         0          0
## SPAC1002.02     2.890e-02       0.326  0.01124 9.919e-01  9.963e-01
## SPAC1002.03c    1.016e-03      -0.339 -9.25600 1.969e-03  3.718e-02
##              deseq_basemean deseq_lfcse deseq_stat deseq_p ebseq_fc
## SPAC1002.01           11.15      0.8209   -1.31600  0.1882   0.5391
## SPAC1002.02           87.42      0.3316   -0.04479  0.9643   1.0571
## SPAC1002.03c        1621.00      0.1387   -1.64100  0.1008   0.8580
##              ebseq_logfc ebseq_postfc ebseq_mean ebseq_ppee ebseq_ppde
## SPAC1002.01     -0.89135       0.5554      11.41     0.4683  5.317e-01
## SPAC1002.02      0.08007       1.0567      88.88     0.9177  8.227e-02
## SPAC1002.03c    -0.22094       0.8581    1637.86     0.6947  3.053e-01
##              ebseq_adjp edger_logcpm edger_lr edger_p limma_ave  limma_t
## SPAC1002.01      0.4683      0.06691 2.745000 0.09757   -0.1955  -2.8320
## SPAC1002.02      0.9177      2.89400 0.007429 0.93130    2.8470   0.1354
## SPAC1002.03c     0.6947      7.09500 3.399000 0.06522    7.0770 -12.5400
##              limma_b limma_p limma_adjp_fdr deseq_adjp_fdr edger_adjp_fdr
## SPAC1002.01  -4.0790 0.07147      1.693e-01      4.186e-01      2.201e-01
## SPAC1002.02  -7.4050 0.90140      9.945e-01      1.000e+00      1.000e+00
## SPAC1002.03c -0.6495 0.00151      2.432e-02      2.682e-01      1.598e-01
##              basic_adjp_fdr   lfc_meta   lfc_var lfc_varbymed    p_meta
## SPAC1002.01       0.000e+00 -1.0520000 7.689e-04   -7.305e-04 1.191e-01
## SPAC1002.02       9.954e-01  0.0007106 3.191e-03    4.491e+00 9.323e-01
## SPAC1002.03c      7.607e-03 -0.2681000 2.166e-03   -8.081e-03 5.584e-02
##                  p_var
## SPAC1002.01  3.753e-03
## SPAC1002.02  9.899e-04
## SPAC1002.03c 2.531e-03

sig_genes <- sm(extract_significant_genes(all_combined, excel=FALSE))
head(sig_genes$limma$ups[[1]], n=3)

##               deseq_logfc deseq_adjp edger_logfc edger_adjp limma_logfc
## SPBC2F12.09c        7.212  5.259e-66       7.170 1.264e-180       7.075
## SPAC22A12.17c       5.855  3.969e-19       5.822  3.155e-57       5.609
## SPAPB1A11.02        6.739  1.894e-06       6.483  1.257e-14       5.606
##               limma_adjp basic_nummed basic_denmed basic_numvar
## SPBC2F12.09c     0.01847        6.250       -1.235    2.211e-02
## SPAC22A12.17c    0.02447        9.396        4.087    2.236e-02
## SPAPB1A11.02     0.01696        1.491       -3.604    6.869e-01
##               basic_denvar basic_logfc basic_t   basic_p basic_adjp
## SPBC2F12.09c     5.043e-01       7.485  16.760 2.432e-03  3.928e-02
## SPAC22A12.17c    9.694e-01       5.309  10.080 8.325e-03  6.437e-02
## SPAPB1A11.02     4.981e-01       5.095   7.708 1.687e-03  3.452e-02
##               deseq_basemean deseq_lfcse deseq_stat   deseq_p ebseq_fc
## SPBC2F12.09c           443.5      0.4123     17.490 1.667e-68   143.63
## SPAC22A12.17c         4289.0      0.6255      9.360 7.945e-21    52.82
## SPAPB1A11.02            21.2      1.2810      5.259 1.447e-07   103.34
##               ebseq_logfc ebseq_postfc ebseq_mean ebseq_ppee ebseq_ppde
## SPBC2F12.09c        7.166       132.23     451.03          0  1.000e+00
## SPAC22A12.17c       5.723        52.65    4359.08          0  1.000e+00
## SPAPB1A11.02        6.691        45.38      21.63          0  0.000e+00
##               ebseq_adjp edger_logcpm edger_lr    edger_p limma_ave
## SPBC2F12.09c           0       5.2210   839.00 1.796e-184     2.592
## SPAC22A12.17c          0       8.4930   264.90  1.479e-59     6.482
## SPAPB1A11.02           0       0.9166    65.83  4.910e-16    -1.192
##               limma_t limma_b   limma_p limma_adjp_fdr deseq_adjp_fdr
## SPBC2F12.09c    19.36  0.8017 0.0004519      1.847e-02      6.176e-66
## SPAC22A12.17c   12.49 -0.3953 0.0015260      2.447e-02      4.660e-19
## SPAPB1A11.02    27.66  0.2698 0.0001667      1.696e-02      2.224e-06
##               edger_adjp_fdr basic_adjp_fdr lfc_meta   lfc_var
## SPBC2F12.09c      1.264e-180      9.147e-03    7.152 0.000e+00
## SPAC22A12.17c      3.155e-57      2.609e-02    5.916 1.123e-01
## SPAPB1A11.02       1.257e-14      6.586e-03    6.137 5.880e-02
##               lfc_varbymed    p_meta     p_var
## SPBC2F12.09c     0.000e+00 1.506e-04 6.807e-08
## SPAC22A12.17c    1.898e-02 5.087e-04 7.762e-07
## SPAPB1A11.02     9.581e-03 5.561e-05 9.255e-09

## Here we see that edger and deseq agree the least:
all_comparisons$comparison$comp

##                wt30_vs_wt120
## limma_vs_deseq        0.9808
## limma_vs_edger        0.9601
## limma_vs_ebseq        0.7944
## limma_vs_basic        0.9779
## deseq_vs_edger        0.9814
## deseq_vs_ebseq        0.8329
## deseq_vs_basic        0.9777
## edger_vs_ebseq        0.9165
## edger_vs_basic        0.9782
## ebseq_vs_basic        0.9776

## And here we can look at the set of 'significant' genes according to various tools:
yeast_sig <- sm(extract_significant_genes(all_combined, excel=FALSE))
yeast_barplots <- sm(significant_barplots(combined=all_combined))
yeast_barplots$limma

yeast_barplots$edger

yeast_barplots$deseq

limma_results <- limma_comparison$all_tables
## The set of comparisons performed
names(limma_results)

## [1] "wt30_vs_wt120"

table <- limma_results$wt30_vs_wt120
dim(table)

## [1] 7039    6

gene_names <- rownames(table)

updown_genes <- get_sig_genes(table, p=0.05, lfc=0.4, p_column="P.Value")

## After (adj)p filter, the up genes table has 1190 genes.

## After (adj)p filter, the down genes table has 1424 genes.

## After fold change filter, the up genes table has 962 genes.

## After fold change filter, the down genes table has 1069 genes.

tt <- please_install("GenomicFeatures")
tt <- please_install("biomaRt")
available_marts <- biomaRt::listMarts(host="fungi.ensembl.org")
available_marts

##            biomart                     version
## 1       fungi_mart      Ensembl Fungi Genes 41
## 2 fungi_variations Ensembl Fungi Variations 41

ensembl_mart <- biomaRt::useMart("fungi_mart", host="fungi.ensembl.org")
available_datasets <- biomaRt::listDatasets(ensembl_mart)
pombe_hit <- grep(pattern="pombe", x=available_datasets[["description"]])
pombe_name <- available_datasets[pombe_hit, "dataset"]
pombe_mart <- biomaRt::useDataset(pombe_name, mart=ensembl_mart)

pombe_goids <- biomaRt::getBM(attributes=c("pombase_transcript", "go_id"),
                              values=gene_names, mart=pombe_mart)
colnames(pombe_goids) <- c("ID", "GO")

## In theory, the above should work with a single function call:
pombe_goids_simple <- load_biomart_go(species="spombe", overwrite=TRUE,
                                      dl_rows=c("pombase_transcript", "go_id"),
                                      host="fungi.ensembl.org")

## Unable to perform useMart, perhaps the host/mart is incorrect: fungi.ensembl.org ENSEMBL_MART_ENSEMBL.

## The available marts are:

## fungi_martfungi_variations

## Trying the first one.

## Unable to perform useDataset, perhaps the given dataset is incorrect: spombe_gene_ensembl.

## Trying instead to use the dataset: spombe_eg_gene

## That seems to have worked, extracting the resulting annotations.

## Finished downloading ensembl go annotations, saving to spombe_go_annotations.rda.

## Saving ontologies to spombe_go_annotations.rda.

## Finished save().

head(pombe_goids_simple)

##               ID         GO
## 1    SPRRNA.50.1           
## 2 SPNCRNA.1095.1           
## 3   SPAC212.11.1 GO:0000784
## 4   SPAC212.11.1 GO:0005634
## 5   SPAC212.11.1 GO:0000166
## 6   SPAC212.11.1 GO:0005524

head(pombe_goids)

##               ID         GO
## 1    SPRRNA.50.1           
## 2 SPNCRNA.1095.1           
## 3   SPAC212.11.1 GO:0000784
## 4   SPAC212.11.1 GO:0005634
## 5   SPAC212.11.1 GO:0000166
## 6   SPAC212.11.1 GO:0005524

## This used to work, but does so no longer and I do not know why.
## pombe <- sm(GenomicFeatures::makeTxDbFromBiomart(biomart="fungal_mart",
##                                                  dataset="spombe_eg_gene",
##                                                  host="fungi.ensembl.org"))

## I bet I can get all this information from ensembl now.
## This was found at the bottom of: https://www.biostars.org/p/232005/
link <- "ftp://ftp.ensemblgenomes.org/pub/release-34/fungi/gff3/schizosaccharomyces_pombe/Schizosaccharomyces_pombe.ASM294v2.34.gff3.gz"
pombe <- GenomicFeatures::makeTxDbFromGFF(link, format="gff3", organism="Schizosaccharomyces pombe",
                                          taxonomyId=4896)

## Import genomic features from the file as a GRanges object ...

## OK

## Prepare the 'metadata' data frame ... OK
## Make the TxDb object ... OK

pombe_transcripts <- as.data.frame(GenomicFeatures::transcriptsBy(pombe))
lengths <- pombe_transcripts[, c("group_name","width")]
colnames(lengths) <- c("ID","width")
## Something useful I didn't notice before:
## makeTranscriptDbFromGFF()  ## From GenomicFeatures, much like my own gff2df()
gff_from_txdb <- GenomicFeatures::asGFF(pombe)
## why is GeneID: getting prefixed to the IDs!?
gff_from_txdb$ID <- gsub(x=gff_from_txdb$ID, pattern="GeneID:", replacement="")
written_gff <- rtracklayer::export.gff3(gff_from_txdb, con="pombe.gff")

## Warning in .local(object, con, format, ...): The phase information is missing. The written file will contain
##   CDS with no phase information.

2.7 GOSeq test

summary(updown_genes)

##            Length Class      Mode
## up_genes   6      data.frame list
## down_genes 6      data.frame list

test_genes <- updown_genes$down_genes
rownames(test_genes) <- paste0(rownames(test_genes), ".1")
lengths$ID <- paste0(lengths$ID, ".1")
goseq_result <- sm(simple_goseq(sig_genes=test_genes, go_db=pombe_goids, length_db=lengths))

head(goseq_result$alldata)

##        category over_represented_pvalue under_represented_pvalue
## 371  GO:0005634               9.433e-44                        1
## 380  GO:0005730               3.700e-33                        1
## 1354 GO:0042254               1.198e-29                        1
## 144  GO:0003674               5.976e-28                        1
## 382  GO:0005737               4.385e-27                        1
## 417  GO:0005829               1.308e-20                        1
##      numDEInCat numInCat                term ontology    qvalue
## 371         100      582             nucleus       CC 1.765e-40
## 380          35       63           nucleolus       CC 3.461e-30
## 1354         26       36 ribosome biogenesis       BP 7.473e-27
## 144          77      534  molecular_function       MF 2.795e-25
## 382          81      585           cytoplasm       CC 1.641e-24
## 417          70      556             cytosol       CC 4.079e-18

goseq_result$pvalue_plots$mfp_plot

test_genes <- updown_genes$up_genes
rownames(test_genes) <- paste0(rownames(test_genes), ".1")
goseq_result <- sm(simple_goseq(sig_genes=test_genes, go_db=pombe_goids, length_db=lengths))

head(goseq_result$alldata)

##       category over_represented_pvalue under_represented_pvalue numDEInCat
## 697 GO:0008150               4.518e-56                        1        128
## 382 GO:0005737               1.306e-50                        1        128
## 417 GO:0005829               6.057e-50                        1        124
## 371 GO:0005634               4.879e-46                        1        122
## 144 GO:0003674               1.268e-44                        1        114
## 938 GO:0016020               1.583e-40                        1         98
##     numInCat               term ontology    qvalue
## 697      548 biological_process       BP 8.453e-53
## 382      585          cytoplasm       CC 1.222e-47
## 417      556            cytosol       CC 3.778e-47
## 371      582            nucleus       CC 2.282e-43
## 144      534 molecular_function       MF 4.744e-42
## 938      416           membrane       CC 4.936e-38

goseq_result$pvalue_plots$bpp_plot

2.8 ClusterProfiler test

clusterProfiler really prefers an orgdb instance to use, which is probably smart, as they are pretty nice. Sadly, there is no pre-defined orgdb for pombe…

## holy crap makeOrgPackageFromNCBI is slow, no slower than some of mine, so who am I to complain.
orgdb <- AnnotationForge::makeOrgPackageFromNCBI(
                            version="0.1", author="atb <abelew@gmail.com>",
                            maintainer="atb <abelew@gmail.com>", tax_id="4896",
                            genus="Schizosaccharomyces", species="pombe")
## This created the directory 'org.spombe.eg.db'
devtools::install_local("org.Spombe.eg.db")
library(org.Spombe.eg.db)
## Don't forget to remove the terminal .1 from the gene names...
## If you do forget this, it will fail for no easily visible reason until you remember
## this and get really mad at yourself.
rownames(test_genes) <- gsub(pattern=".1$", replacement="", x=rownames(test_genes))
pombe_goids[["ID"]] <- gsub(pattern=".1$", replacement="", x=pombe_goids[["ID"]])
cp_result <- simple_clusterprofiler(sig_genes=test_genes, do_david=FALSE, do_gsea=FALSE,
                                    de_table=all_combined$data[[1]],
                                    orgdb=org.Spombe.eg.db, orgdb_to="ALIAS")
cp_result[["pvalue_plots"]][["ego_all_mf"]]
## Yay bar plots!

## Get rid of those stupid terminal .1s.
rownames(test_genes) <- gsub(pattern=".1$", replacement="", x=rownames(test_genes))
pombe_goids[["ID"]] <- gsub(pattern=".1$", replacement="", x=pombe_goids[["ID"]])
tp_result <- sm(simple_topgo(sig_genes=test_genes, go_db=pombe_goids, pval_column="limma_adjp"))

tp_result[["pvalue_plots"]][["mfp_plot_over"]]

tp_result[["pvalue_plots"]][["bpp_plot_over"]]

## Get rid of those stupid terminal .1s.
rownames(test_genes) <- gsub(pattern=".1$", replacement="", x=rownames(test_genes))
pombe_goids[["ID"]] <- gsub(pattern=".1$", replacement="", x=pombe_goids[["ID"]])
## universe_merge is the column in the final data frame when.
## gff_type is the field in the gff file providing the id, this may be redundant with
## universe merge, that is something to check on...
gst_result <- sm(simple_gostats(sig_genes=test_genes, go_db=pombe_goids, universe_merge="id",
                                gff_type="gene",
                                gff="pombe.gff", pval_column="limma_adjp"))

pander::pander(sessionInfo())

R version 3.5.1 (2018-07-02)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.utf8, LC_NUMERIC=C, LC_TIME=en_US.utf8, LC_COLLATE=en_US.utf8, LC_MONETARY=en_US.utf8, LC_MESSAGES=en_US.utf8, LC_PAPER=en_US.utf8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.utf8 and LC_IDENTIFICATION=C

attached base packages: stats4, parallel, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: edgeR(v.3.22.5), variancePartition(v.1.10.4), fission(v.0.114.0), SummarizedExperiment(v.1.10.1), DelayedArray(v.0.6.6), BiocParallel(v.1.14.2), matrixStats(v.0.54.0), GenomicRanges(v.1.32.7), GenomeInfoDb(v.1.16.0), IRanges(v.2.14.12), S4Vectors(v.0.18.3), hpgltools(v.2018.11), foreach(v.1.4.4), ruv(v.0.9.7), bindrcpp(v.0.2.2), Biobase(v.2.40.0) and BiocGenerics(v.0.26.0)

loaded via a namespace (and not attached): SparseM(v.1.77), rtracklayer(v.1.40.6), pkgmaker(v.0.27), tidyr(v.0.8.2), ggplot2(v.3.1.0), acepack(v.1.4.1), bit64(v.0.9-7), knitr(v.1.20), data.table(v.1.11.8), rpart(v.4.1-13), RCurl(v.1.95-4.11), doParallel(v.1.0.14), snow(v.0.4-3), GenomicFeatures(v.1.32.3), preprocessCore(v.1.42.0), callr(v.3.0.0), cowplot(v.0.9.3), usethis(v.1.4.0), RSQLite(v.2.1.1), bit(v.1.1-14), enrichplot(v.1.0.2), xml2(v.1.2.0), httpuv(v.1.4.5), assertthat(v.0.2.0), viridis(v.0.5.1), hms(v.0.4.2), evaluate(v.0.12), promises(v.1.0.1), DEoptimR(v.1.0-8), progress(v.1.2.0), caTools(v.1.17.1.1), igraph(v.1.2.2), DBI(v.1.0.0), geneplotter(v.1.58.0), htmlwidgets(v.1.3), purrr(v.0.2.5), crosstalk(v.1.0.0), selectr(v.0.4-1), dplyr(v.0.7.8), backports(v.1.1.2), annotate(v.1.58.0), biomaRt(v.2.36.1), blockmodeling(v.0.3.1), remotes(v.2.0.2), withr(v.2.1.2), ggforce(v.0.1.3), packrat(v.0.4.9-3), robustbase(v.0.93-3), checkmate(v.1.8.5), GenomicAlignments(v.1.16.0), prettyunits(v.1.0.2), cluster(v.2.0.7-1), DOSE(v.3.6.1), lazyeval(v.0.2.1), crayon(v.1.3.4), genefilter(v.1.62.0), pkgconfig(v.2.0.2), labeling(v.0.3), units(v.0.6-1), tweenr(v.1.0.0), nlme(v.3.1-137), pkgload(v.1.0.2), nnet(v.7.3-12), devtools(v.2.0.1), bindr(v.0.1.1), rlang(v.0.3.0.1), registry(v.0.5), doSNOW(v.1.0.16), directlabels(v.2018.05.22), rprojroot(v.1.3-2), graph(v.1.58.2), rngtools(v.1.3.1), Matrix(v.1.2-15), base64enc(v.0.1-3), geneLenDataBase(v.1.16.0), ggridges(v.0.5.1), processx(v.3.2.0), viridisLite(v.0.3.0), bitops(v.1.0-6), KernSmooth(v.2.23-15), pander(v.0.6.3), Biostrings(v.2.48.0), EBSeq(v.1.20.0), blob(v.1.1.1), doRNG(v.1.7.1), stringr(v.1.3.1), qvalue(v.2.12.0), readr(v.1.1.1), scales(v.1.0.0), memoise(v.1.1.0), magrittr(v.1.5), plyr(v.1.8.4), gplots(v.3.0.1), bibtex(v.0.4.2), gdata(v.2.18.0), zlibbioc(v.1.26.0), compiler(v.3.5.1), RColorBrewer(v.1.1-2), lme4(v.1.1-19), DESeq2(v.1.20.0), Rsamtools(v.1.32.3), cli(v.1.0.1), ade4(v.1.7-13), XVector(v.0.20.0), ps(v.1.2.1), htmlTable(v.1.12), Formula(v.1.2-3), MASS(v.7.3-51.1), mgcv(v.1.8-25), tidyselect(v.0.2.5), stringi(v.1.2.4), highr(v.0.7), yaml(v.2.2.0), GOSemSim(v.2.6.2), locfit(v.1.5-9.1), latticeExtra(v.0.6-28), ggrepel(v.0.8.0), grid(v.3.5.1), fastmatch(v.1.1-0), tools(v.3.5.1), rstudioapi(v.0.8), foreign(v.0.8-71), gridExtra(v.2.3), farver(v.1.0), Rtsne(v.0.15), ggraph(v.1.0.2), digest(v.0.6.18), rvcheck(v.0.1.1), shiny(v.1.2.0), quadprog(v.1.5-5), Rcpp(v.1.0.0), later(v.0.7.5), httr(v.1.3.1), AnnotationDbi(v.1.42.1), genoPlotR(v.0.8.7), colorspace(v.1.3-2), rvest(v.0.3.2), XML(v.3.98-1.16), fs(v.1.2.6), topGO(v.2.32.0), splines(v.3.5.1), RBGL(v.1.56.0), plotly(v.4.8.0), sessioninfo(v.1.1.1), xtable(v.1.8-3), jsonlite(v.1.5), nloptr(v.1.2.1), corpcor(v.1.6.9), UpSetR(v.1.3.3), testthat(v.2.0.1), R6(v.2.3.0), Vennerable(v.3.1.0.9000), Hmisc(v.4.1-1), pillar(v.1.3.0), htmltools(v.0.3.6), mime(v.0.6), glue(v.1.3.0), minqa(v.1.2.4), clusterProfiler(v.3.8.1), DESeq(v.1.32.0), codetools(v.0.2-15), fgsea(v.1.6.0), pkgbuild(v.1.0.2), lattice(v.0.20-38), tibble(v.1.4.2), sva(v.3.28.0), pbkrtest(v.0.4-7), curl(v.3.2), BiasedUrn(v.1.07), colorRamps(v.2.3), gtools(v.3.8.1), zip(v.1.0.0), GO.db(v.3.6.0), openxlsx(v.4.1.0), survival(v.2.43-1), limma(v.3.36.5), rmarkdown(v.1.10), desc(v.1.2.0), munsell(v.0.5.0), DO.db(v.2.9), GenomeInfoDbData(v.1.1.0), iterators(v.1.0.10), goseq(v.1.32.0), reshape2(v.1.4.3) and gtable(v.0.2.0)